The Crossroads of Innovation and Privacy: Private Synthetic Data for Generative AI
![A flow chart with four successive blocks. Starting with a data owner, private data is provisioned to train a language model with differential privacy. The language model is subsequently prompted to generate novel synthetic data resembling the private data. This data can be used for down-stream applications such as machine learning, feedback analysis or statistical analysis.](https://www.microsoft.com/en-us/research/uploads/prodnew/2024/05/NEW_PSD-for-Gen-AI-2024-BlogHeroFeature-1400x788-1.png)
Introduction
In today’s data-driven world, organizations strive to leverage data to train and adapt AI models. However, this pursuit often faces an important challenge: balancing the value of data with the need to safeguard individuals’ right to